National University of Singapore (NUS) researchers along with industry partners Soitec, NXP Semiconductors and Dolphin Design have unveiled a new class of silicon systems to enhance the energy efficiency of AI-connected devices by leaps and bounds.
The innovation has been demonstrated in fully-depleted silicon-on-insulator (FD-SOI) technology, and can be applied to the design and fabrication of advanced semiconductor components for AI applications.
NUS said this chip technology has the potential to extend the battery life of wearables and smart objects by a factor of 10, support intense computational workloads for use in Internet of Things (IoT) applications, and halve the power consumption associated with wireless communications with the cloud.
It will be promoted through the FD-SOI & IoT Industry Consortium, to accelerate industry adoption by lowering the design barrier to entry in FD-SOI chips.
Improving performance
Professor Massimo Alioto, from the NUS College of Design and Engineering’s Department of Electrical and Computer Engineering said the research "uniquely" allows to reduce average power and improve peak performance.
He explained that IoT devices often operate on a very limited power budget, and hence require extremely low average power to efficiently perform regular tasks such as physical signal monitoring.
At the same time, high peak performance is demanded to process occasional signal events with computationally-intensive AI algorithms, he added.
“The applications are wide-ranging and include smart cities, smart buildings, Industry 4.0, wearables and smart logistics. The remarkable energy improvements obtained in the FD-fAbrICS program are a game changer in the area of battery-powered AI devices, as they ultimately allow us to move intelligence from conventional cloud to smart miniaturised devices,” said Prof Alioto, who is also the Director of the FD-fAbrICS (FD-SOI Always-on Intelligent & Connected Systems) joint lab where the new suite of technologies was engineered.
Research conducted by the NUS FD-fAbrICS joint lab showed that their FD-SOI chip technology can be deployed at scale with enhanced design and system integration productivity for lower cost, faster market reach, and rapid industry adoption.
The NUS team is now looking into developing new classes of intelligent and connected silicon systems that could support larger AI model sizes for generative AI applications.
NUS added that the resulting decentralisation of AI computation from cloud to distributed devices will simultaneously preserve privacy, keep latency at a minimum, and avoid wireless data deluge under the simultaneous presence of a plethora of devices.
